Ilya Nemenman: Large N in neural data -- expecting the unexpected
Talk by Ilya Nemenman, Departments of Physics and Biology, Emory University. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley.
Audio/Visual sound, color
Recently it has become possible to directly measure simultaneous collective states of many biological components, such as neural activities, genetic sequences, or gene expression profiles. These data are revealing striking results, suggesting, for example, that biological systems are tuned to criticality, and that effective models of these systems based on only pairwise interactions among constitutive components provide surprisingly good fits to the data. We will explore a handful of simplified theoretical models, largely focusing on statistical mechanics of Ising spins, that suggest plausible explanations for these observations. Specifically, I will argue that, at least in certain contexts, these intriguing observations should be expected in multivariate interacting data in the thermodynamic limit of many interacting components.